Data-Driven Analysis and Interpolation of Optical Material Properties

Abstract

Reproducing the characteristic appearance of materials digitally is of considerable importance for the creation of photo-realistic images. However, due to the high complexity of many real-world materials, modeling them accurately is a difficult problem. Therefore, data-driven techniques have received considerable attention in the last years. In these approaches, the optical material properties from actual material samples are measured. This enables their faithful reproduction and the creation of synthetic images which are difficult to distinguish from actual photographs of the material. Still, these techniques have not yet found wide-spread practical application. This is mainly due to the fact that the measurement process is still time-consuming and expensive and that the resulting datasets are large and difficult to process and edit. This dissertation is split into two parts which address these questions by providing more efficient representations and interpolation techniques for optical material properties.
The first part is concerned with representations for optical material properties and techniques to derive these representations from measurements. First, we investigate considerable improvements of the compression speed of the classical PCA based representation of BTFs. For this, we describe a GPU accelerated technique to compute the PCA of very large data-matrices. Then, we introduce a compact representation for BRDFs, based on a PARAFAC tensor decomposition, and for BTFs, based on a sparse tensor decomposition. Finally, we develop techniques to reconstruct the reflectance behavior of a material from a sparse and irregular input sampling either using a representation via a heightfield and a mixture of analytical BRDFs or by fitting a sum of separable functions to the sparse samples.
In the second part, we explore material editing approaches based on the interpolation between measured exemplars. For this, we develop data-driven interpolation techniques for BRDFs, textures and BTFs. We demonstrate that it is possible to create believable interpolation sequences even for materials with complex feature topology, spatially varying reflectance behavior and a meso-structure resulting in strong parallaxes. These techniques provide the foundation for an intuitive and powerful material editing approach, which gives the end user the ability to create a new material by combining the characteristics of several measured samples.

Bibtex

@PHDTHESIS{ruiters-2014-phd,
author = {Ruiters, Roland},
title = {Data-Driven Analysis and Interpolation of Optical Material Properties},
type = {Dissertation},
year = {2014},
school = {Universit{\"a}t Bonn},
abstract = {Reproducing the characteristic appearance of materials digitally is of considerable importance for
the creation of photo-realistic images. However, due to the high complexity of many real-world
materials, modeling them accurately is a difficult problem. Therefore, data-driven techniques have
received considerable attention in the last years. In these approaches, the optical material
properties from actual material samples are measured. This enables their faithful reproduction and
the creation of synthetic images which are difficult to distinguish from actual photographs of the
material. Still, these techniques have not yet found wide-spread practical application. This is
mainly due to the fact that the measurement process is still time-consuming and expensive and that
the resulting datasets are large and difficult to process and edit. This dissertation is split into
two parts which address these questions by providing more efficient representations and
interpolation techniques for optical material properties.
The first part is concerned with representations for optical material properties and techniques to
derive these representations from measurements. First, we investigate considerable improvements of
the compression speed of the classical PCA based representation of BTFs. For this, we describe a GPU
accelerated technique to compute the PCA of very large data-matrices. Then, we introduce a compact
representation for BRDFs, based on a PARAFAC tensor decomposition, and for BTFs, based on a sparse
tensor decomposition. Finally, we develop techniques to reconstruct the reflectance behavior of a
material from a sparse and irregular input sampling either using a representation via a heightfield
and a mixture of analytical BRDFs or by fitting a sum of separable functions to the sparse samples.
In the second part, we explore material editing approaches based on the interpolation between
measured exemplars. For this, we develop data-driven interpolation techniques for BRDFs, textures
and BTFs. We demonstrate that it is possible to create believable interpolation sequences even for
materials with complex feature topology, spatially varying reflectance behavior and a meso-structure
resulting in strong parallaxes. These techniques provide the foundation for an intuitive and
powerful material editing approach, which gives the end user the ability to create a new material by
combining the characteristics of several measured samples.},
url = {http://hss.ulb.uni-bonn.de/2015/4002/4002.htm}
}